In construction, stakeholders of extended project team play a key role in the overall project performance. Successful integration of stakeholders demands for good management practices at strategic, operational and project levels. Targets and measures to improve the stakeholder performance encourage the creativity and willingness of stakeholders of extended project team to develop the better ways to achieve the project objectives. This paper presents a generic descriptive method, showing how stakeholder`s ability and influence impacts on project performance in the construction sector. The findings of a series of interviews with key informants are presented and the following main conclusion is drawn: improving project performance through stakeholder`s contribution and measuring their performance can strengthen the project performance. This innovative approach which redefines the process of improving the project performance in construction projects will be of interest to those who intend to manage the projects in practice as well as to those who interested in advancing theory.

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project`s total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel and EVOLVERTM V.5.5. It provides projects` owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.

Interface management problems inherent in construction projects hamper their successful delivery. Therefore, this study aimed at determining the most important project interfaces in construction works in Nigeria in terms of most significant potential impacts, so that management attention are objectively focused on potential highest impacting project interfaces. From a review of literature, 28 project interfaces management issues were identified and categorized. Structured questionnaires were used to collect data concerning the impact (estimated losses to the project in terms of cost) and probability of occurrence of the identified interfaces. The interfaces were ranked using their computed Matrix Scores (MS). The results reveal that "project-workers interfaces problem manifested in use of inappropriate mixes" is the highest impacting. A ranking of the interface categories also reveal that the interfaces at the execution phase of a project (MS

Success of the construction companies is based on the successful completion of projects within the agreed cost and time limits. Artificial neural networks (ANN) have recently attracted much attention because of their ability to solve the qualitative and quantitative problems faced in the construction industry. For the estimation of cost and duration different ANN models were developed. The database consists of data collected from completed projects. The same data is normalised and used as inputs and targets for developing ANN models. The models are trained, tested and validated using MATLAB R2013a Software. The results obtained are the ANN predicted outputs which are compared with the actual data, from which deviation is calculated. For this purpose, two successfully completed highway road projects are considered. The Nftool (Neural network fitting tool) and Nntool (Neural network/ Data Manager) approaches are used in this study. Using Nftool with trainlm as training function and Nntool with trainbr as the training function, both the Projects A and B have been carried out. Statistical analysis is carried out for the developed models. The application of neural networks when forming a preliminary estimate, would reduce the time and cost of data processing. It helps the contractor to take the decision much easier.

Construction industry is an aggregate of information that diverse information is integrated and controlled. To implement successful construction projects, it can be said that the information management is very important. In particular, because information of construction sites is controlled in a form of documents, importance of the document management in construction has been increased. But, by controlling information through documents, there are difficult problems in writing and classification of the documents and preservation and utilization of the information. Also, due to incompletion of the information management system, difficulty in systematic info management arises. For this reason, this study intends to suggest the document information breakdown structure for controlling document info efficiently which is generated at construction sites. For this, through the examination of preceding studies, establishment of the concept of the document info breakdown structure, the space breakdown structure, and the info breakdown structure, availability of document information is intended to heighten.

This study presents the results of our analysis and recommendations for process and productivity improvements. The project studied consists of a 5-story research building, with a structure of steel frames supporting concrete slabs. The observations focused on the analysis of the overall erection and framing process. The methods used for the analysis consisted in intensive visits on site, where construction processes were observed in term of resources, activities, durations, materials` handling procedures, and technology used. Back to the office, authors used the information captured to model the different trades` activities, using work sampling and 5-minute rating technique. The work sampling provides insight into the activity, hence allowing for process improvements. The productivity of various trades is strongly dependent on the organization of the work process and work site conditions. Improving the productivity of the entire project or company is not possible until everyone is committed to improvement.